---
title: "Loan Application Dashboard"
author: "Dustin T."
date: "2021 M01 8"
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    vertical_layout: fill
    social: ["twitter", "facebook","menu","github","linkedin"]
    source_code: embed
---


```{r setup, include=FALSE}
library(flexdashboard)
library(knitr)
library(DT)
library(rpivotTable)
library(ggplot2)
library(plotly)
library(dplyr)
library(openintro)
library(highcharter)
library(ggvis)
# Machine learnning method packages
library(ROCR)
library(pROC)
library(caret)
library(MASS)
#library(sjPlot)
```

```{r readDat}
data <- read.csv("train.csv")
#preprocess data
var.has.na <- lapply(data, function(x){any(is.na(x))})
num_na <- which( var.has.na == TRUE )	
per_na <- num_na/dim(data)[1] 
data <- data[complete.cases(data),]
```

```{r}
mycolors <- c("blue", "#FFC125", "darkgreen", "darkorange")
```

Data Viz {data-icon="fa-globe"}
==========================================

Row
------------------------------------------


### Credit History and Status.

```{r introduction}
valueBox(paste("Loaning System"),
         color = "warning")
```


### Bank Customers

```{r total_customers}
valueBox(length(data$Loan_ID),
         icon = 'fa-user')
```


### **Mean Applicant's Income**

```{r somegauges}
gauge(round(mean(data$ApplicantIncome),
            digits = 2),
            min = 0,
            max = max(data$ApplicantIncome),
            gaugeSectors(success = c(4000,6000),
                         warning = c(2000,4000),
                         danger = c(0,2000),
                         colors = c('blue','yellow', 'red')))
```


### Members have Pending Loans.

```{r}
valueBox(sum(data$Loan_Status == "Y"),
         icon = 'fa-user-times')
```


### Members have no loans.

```{r Loan_status}
valueBox(sum(data$Loan_Status == "N"),
         icon = 'fa-user-plus')

```


### Members with a Good Credit History

```{r Credit History}
valueBox(sum(data$Credit_History == 1),
         icon = 'fa-user-plus')

```


### Members with a Bad Credit History

```{r Florida}
valueBox(sum(data$Credit_History == 0),
         icon = 'fa-user-times')
```


Row
----------------------------------


### Loan Amount Term

```{r term}
p1 <- data %>%
  group_by(Loan_Amount_Term) %>%
  summarise(count = n()) %>%
  ungroup() %>%
  plot_ly(x = ~Loan_Amount_Term,
          y = ~count,
          color = "cyan",
          type = 'bar') %>%
  layout(xaxis = list(title="Loaning Duration"),
       yaxis = list(title="Count"))


p1
```


### Piechart of loan status

```{r pie1}
p2 <- data %>%
  group_by(Loan_Status) %>%
  summarise(count = n()) %>%
  ungroup() %>%
  plot_ly(labels = ~Loan_Status,
          values = ~count,
          marker = list(colors = mycolors)) %>%
  add_pie(hole = .2) %>%
  layout(xaxis = list(zeroline = F,
                      showline = F,
                      showticklabels = F,
                      showgrid = F),
         yaxis = list(zeroline = F,
                      showline = F,
                      showticklabels = F,
                      showgrid=F))


p2

```


### Piechart of Credit History

```{r boxplot}
p2 <- data %>%
  group_by(Credit_History) %>%
  summarise(count = n()) %>%
  ungroup() %>%
  plot_ly(labels = ~Credit_History,
          values = ~count,
          marker = list(colors = mycolors)) %>%
  add_pie(hole = .2) %>%
  layout(xaxis = list(zeroline = F,
                      showline = F,
                      showticklabels = F,
                      showgrid = F),
         yaxis = list(zeroline = F,
                      showline = F,
                      showticklabels = F,
                      showgrid=F))



p2

```

### Scatter plot.

```{r}
p4 <- plot_ly(data, x =~ApplicantIncome) %>%
  add_markers(y = ~LoanAmount,
              text = ~paste("Loan Amount: ", LoanAmount),
              showlegend = F) %>%
  add_lines(y = ~fitted(loess(LoanAmount ~ApplicantIncome)),
            name = "Loess Smoother",
            color = I("#FFC125"),
            showlegend = T,
            line = list(width = 5)) %>%
  layout(xaxis = list(title="ApplicantIncome"),
         yaxis = list(title= "LoanAmount"))
p4

```


Data Table {data-icon="fa-wrench"}
===========================================
```{r}
datatable(data,
          caption = "Loan Credit History",
          rownames = T,
          filter = "top",
          options = list(pageLength = 100))
```

Pivot Table {data-icon="fa-sort-amount-desc"}
=============================================
```{r}
rpivotTable(data[,-1], 
            aggregatorName = "Count",
            cols = "Credit_History",
            rows = "Loan_Status",
            rendererName = "Heatmap")
```

SummaryReport {data-orientation=columns, data-icon="fa-diamond"} 
============================================
Column { data-width = 50}
----------------------------------------

### Max Loan Amount Term

```{r}
valueBox(max(data$Loan_Amount_Term),
         icon = "fa-user")
```

### Average Applicant's Income

```{r}
valueBox(round(mean(data$ApplicantIncome),
               digits = 2),
         icon = "fa-area-chart")
```

### Relationship between Credit History and Loan Status.

```{r}
datatable(as.data.frame(table(history=data$Credit_History,status=data$Loan_Status)))
```

Column 
-------------------------------------------
Report

* This is a report on `r length(data$Loan_ID)` bank Lonees.

* The average Applicant Income was `r mean(data$ApplicantIncome)`.

* The average Loan Amount was `r mean(data$LoanAmount)`.

This report was generated on `r format(Sys.Date(), format = "%B %d, %Y")`.